ArticleiScience2025
How to predict effective drug combinations - moving beyond synergy scores.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A dual granular balanced deep forest model for effective drug combination prediction.iScience · 2026Article
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- Combined Antineoplastic Effects of Metformin, Boric Acid and Resveratrol in SKOV3 Ovarian Cancer Cells.Biomedicines · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To improve our understanding of multi-drug therapies, cancer cell line panels screened with drug combinations are frequently studied using machine learning (ML). ML models trained on such data typically focus on predicting synergy scores that support drug development and repurposing efforts but have limitations when deriving personalized treatment recommendations. To simulate a more realistic personalized treatment scenario, we pioneer ML models that make dose-specific predictions of the relative growth inhibition (instead of synergy scores), and that can be applied to previously unseen cell lines. Our approach is highly flexible: it enables the reconstruction of dose-response curves and matrices, as well as various measures of drug sensitivity (and synergy) from model predictions, which can finally even be used to derive cell line-specific prioritizations of both mono- and combination therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.